1,721,047 research outputs found
Global motion planning under uncertain motion, sensing, and environment map
Get PDFUncertainty in motion planning is often caused by three main sources: motion error, sensing error, and imperfect environment map. Despite the significant effect of all three sources of uncertainty to motion planning problems, most planners take into account only one or at most two of them. We propose a new motion planner, called Guided Cluster Sampling (GCS), that takes into account all three sources of uncertainty for robots with active sensing capabilities. GCS uses the Partially Observable Markov Decision Process (POMDP) framework and the point-based POMDP approach. Although point-based POMDPs have shown impressive progress over the past few years, it performs poorly when the environment map is imperfect. This poor performance is due to the extremely high dimensional state space, which translates to the extremely large belief space B. We alleviate this problem by constructing a more suitable sampling distribution based on the observations that when the robot has active sensing capability, B can be partitioned into a collection of much smaller sub-spaces, and an optimal policy can often be generated by sufficient sampling of a small subset of the collection. Utilizing these observations, GCS samples B in two-stages, a subspace is sampled from the collection and then a belief is sampled from the subspace. It uses information from the set of sampled sub-spaces and sampled beliefs to guide subsequent sampling. Simulation results on marine robotics scenarios suggest that GCS can generate reasonable policies for motion planning problems with uncertain motion, sensing, and environment map, that are unsolvable by the best point-based POMDPs today. Furthermore, GCS handles POMDPs with continuous state, action, and observation spaces. We show that for a class of POMDPs that often occur in robot motion planning, given enough time, GCS converges to the optimal policy. To the best of our knowledge, this is the first convergence result for point-based POMDPs with continuous action space
Analysis and Control of a Dissipative Spring-Mass Hopper with Torque Actuation
Get PDFAbstract — It has long been established that simple spring-mass models can accurately represent the dynamics of legged locomotion. Existing work in this domain, however, almost exclusively focuses on the idealized Spring-Loaded Inverted Pendulum (SLIP) model and neglects passive dissipative effects unavoidable in any physical robot or animal. In this paper, we extend on a recently proposed analytic approximation to the stance trajectories of a dissipative SLIP model to analyze stability properties of a planar hopper with a single rotary actuator at the hip. We first describe how a suitably chosen torque controller can compensate for damping losses, maintaining the same energy level across strides and hence reducing the return map to a single dimension. We then identify and characterize equilibrium points for this return map under a fixed leg placement policy and show that “uncontrolled ” asymptotic stability is feasible for this energy-regulated system. Subsequent presentation of simulation evidence establishes that the predictions of this approximate model are consistent with the exact plant model. The paper concludes with the application of our energy-regulation scheme to the design of a task-level gait controller that uses explicit leg placement commands in conjunction with the hip torque. I
Field robots
No full textField Robots are machines that work in unstructured environments, including under water, in mines, in forests and on farms, and in the air. These applications involve both advanced ideas in robotics and careful attention to engineering details. This paper discusses the overall challenges of field robotics and the current state of the art. The paper is not a thorough survey, nor a tutorial; but is instead intended to serve as a discussion starter for further design and development
Decentralized Data Fusion and Control in Active Sensor Networks
No full textThe paper presents an algorithm for Bayesian decentralized data fusion (BDDF) and its extension to informationtheoretic control. The algorithm is stated for a feature represented by a general probability density function. Several specific representations are then considered -- Gaussian, discrete, Certainty Grid, and hybrid. Well known algorithms for these representations are shown to fit the general BDDF pattern. Stating the algorithms in Bayesian terms has a practical advantage of allowing a generic software implementation. It is also hoped that a clear general formulation will stimulate extensions to efficient non-parametric representations of arbitrary distributions. The algorithms are described in the context of the Active Sensor Network architecture -- a modular framework for decentralized cooperative data fusion and control. The approach is illustrated with the results of two deployment scenarios with an indoor sensor network
- …
